Electronic trading systems provide buyers and sellers, collectively referred to as traders, with best offer data and best bid data for a given tradable object in addition to other useful market data such as additional order information besides the best offer and bid data, fill information, the last traded price (“LTP”) and the last traded quantity (“LTQ”). The LTQ generally reflects the quantity of the last match performed by the exchange's matching engine, while the LTP generally reflects the price level of the match.
As used herein, a tradable object simply refers to anything that can be traded with a price and/or quantity. Examples of tradable objects may include, but are not limited to, all types of traded events, goods and/or financial products, such as, for example, stocks, options, bonds, futures, currency, and warrants, as well as funds, derivatives and collections of the foregoing. Moreover, tradable objects may include all types of commodities, such as grains, energy, and metals. A tradable object may be “real”, such as products that are listed by an exchange for trading, or “synthetic”, such as a combination of real products that is created by the trader. A tradable object could actually be a combination of other tradable objects, such as a class of tradable objects.
Technical analysis may be used to assist traders in making their trades. Generally speaking, technical analysis is the process of analyzing a tradable object's present and historical prices and other related data in an effort to determine, among other things, probable future prices. Technical analysis may result in detecting market trends, predicting turning points and market direction. Traders can study the information and take certain actions based on that information. Even if forecasting prices is not completely accurate, given the dynamic and often unpredictable nature of the markets, technical analysis may still be useful in reducing the risks and improving the profits. Although it is not the only one, a primary tool of technical analyses is a chart.
One area of trading where traders might use technical analysis is spread trading. Spread trading is the buying and/or selling of two or more tradable objects, the typical purpose of which is to capitalize on changes or movements in the relationships between the tradable objects. The relationship between the tradable objects might be based on a real relationship or simply a perceived one by the trader. A spread trade could involve buying two or more tradable objects, buying and selling two or more tradable objects, selling two or more tradable objects or some combination thereof. In some financial areas, such as in the futures industry, the tradable objects being spread might be contracts for different delivery months (e.g., expiration dates) of the same tradable object or contracts of the same tradable object at different strike prices. Sometimes, spread trading involves different tradable objects or the same tradable object on different exchanges.
Here are some simple examples of spread trading: A trader might spread trade a June corn tradable object and a December corn tradable object. To do so might involve buying the June corn tradable object, based on the December market, and selling the December corn tradable object, or vice versa. Spreading can also be done based on other relationships besides calendar months. One such example would be trading a 10-year note and a 5-year note. According to these two examples given directly above, each spread has two legs. As used herein, legs refer to the portions of the trades associated with each individual tradable object, which is also referred to as an outright market. For instance, the June/December corn calendar spread has two legs, the June corn tradable object makes up one leg and the December corn tradable object makes up the other leg.
Spread trading can also involve more than two legs. For example, a well-known strategy called the butterfly involves buying a near month tradable object, selling two middle month tradable objects, and buying a far month tradable object. An example might be buying one March corn tradable object, selling two June corn tradable objects, and buying one December corn tradable object. The butterfly strategy in this example has three legs. The March corn tradable object makes up one leg, the June corn tradable object makes up a second leg, and the December corn tradable object makes up a third leg. There are many other types of well-known strategies in addition to the butterfly that involve more than two legs.
According to conventional technical analysis techniques, an estimated last traded price (“estimated LTP”) for a spread is computed and used in analyzing the value of the spread. The estimated LTP represents a price at which a trader might have been able to buy or sell a spread given the last traded prices of the legs. In particular, when a trade occurs in one leg, a conventional software application looks to the last traded price in each of the other legs, and together with the most recent traded price in the leg that traded computes the estimated LTP of the spread. This process repeats every time a trade occurs in one of the legs. Although the estimated LTP values consist of only estimated prices at which a spread might possibly have been bought or sold, they can provide the trader with a historical trail of valuable market information that may be used in determining the trader's next action.
Often times, the estimated LTP values of the spread are displayed in a chart to the trader. There are many styles of charts that may be used and they may include an area, bar, candlestick, or line chart. Using a chart, traders may visually spot trends and react accordingly. The estimated LTP values might also be displayed in some other graphical form or they may be displayed in a textual or numerical format. In some instances, these values might not be displayed at all, but rather used by an automated or semiautomatic trading tool in carrying out its particular trading strategy.
Given liquid markets where trades are frequently occurring in each of the legs, conventional technical analysis techniques may provide valuable and useful information to the trader. This is because the calculation uses the last traded price for each leg, which update often in liquid markets. Nonetheless, if the trades begin to lag the market's movement in any of the legs, the estimated LTP of the spread can quickly become outdated and inaccurate. As such, using conventional techniques, the estimated LTP of the spread is not really a true representation of the current spread value. Accordingly, conventional technical analysis techniques fall short of providing the trader with a complete and accurate picture of the value of a spread.
There is a need for an improved way to estimate the value of a spread.